First of all, let me get one thing out of the way: the notion that aesthetics is about pleasure (“looks nice”) is a simplistic one.

Often, in these discussions, aesthetics is treated like sugar (or bacon?) on top of a perfectly fine working thing, which, when added, can somewhat improve the experience, yet, used in too high amount, actually spoils the dish.

This simplistic view ignores that aesthetic value goes way beyond pure pleasure or decoration, and arises from a couple of other factors, some of which are novelty, allusions, cultural references, unfolding of perceptual experiences… maybe something like “sensual and cultural intriguingness” could be a good alternate term.

In design specifically — per definition — aesthetic value also arises from a tight interplay of function and form. That “designer” chair that looks great, but is not comfortable to sit on? Not good design! It is simple as that.

Which brings me to my central point here: the misguided idea that aesthetics is somehow obstructive to understanding.

Granted, I have seen aesthetic data visualizations with zero informative value. But, likewise, I have also seen loads of non-aesthetic ones that are plain misleading or ridiculously hard to read; likewise, I have seen striking visuals which were highly informative and also fairly ugly or bland informative ones.

I have seen literally any combination of aesthetics and informativeness.

What if… these two are maybe actually independent dimensions, not arch enemies?

Let’s take this point further: what if there are actually many more dimensions of qualities a data visualization can have?

Here’s a few examples:

How quickly do you get to insights?

How deep are the insights gained?

How memorable is the visualization?

Does it use learned idioms vs innovative visual encoding?

What is its impact? What do people do with it? What is their reaction?

How much does it draw you in?

Did you remember it two weeks later? Did you remember the key insights two weeks later?

How much time do people spend with it?

Is it for everyone or a niche group of people?

Does it confirm expectations or present new insights?

How complex is the information?

What is the tone like?

What are the cultural references it alludes to? In which visual tradition does it place itself? Does it use visual jargon?

Does it make you laugh? Does it make you cry? How does it make you feel?

Is the experience the same for everyone, or different depending on your choices or perspective?…

This is just a tiny sample of dimensions we could look at.

My main point here is, that each visualization will land somewhere in that point-space spanned by all these dimensions, and none of them are a priori opposed to each other.

And, in the end, the fact if it will be a good visualization will be determined by if the creator makes the right choices, in the given context, for the given audience and purpose.

This, of course, is a designerly way of thinking, but, you know what: I think, ultimately, it holds true for artists as well.

Honestly, I don’t care what a data visualization piece was “meant” to be. Let’s talk about what it achieves, on which levels, for whom, and how!

Is it a data visualization that “engages through a metaphor to raise awareness for a complex, important topic, is hard to get into on first sight, but then delivers deep insights for a small fraction of users”?

Or a “visual pun good for a 10 second view, designed to go viral”?

Or “clever appropriation of forms known from financial data to qualified self data”?

Or an “overaestheticized, sloppy treatment of run-of-the-mill data”?

TL;DR Data visualization is a medium. Let’s take it serious as a rich cultural form of expression and understand all the facets involved. Asking for a stricter separation of art and design (and promoting simplistic views of those disciplines) is not helping here.